Spatial up-sampling of discretely sampled visual data, often referred to as super-resolution, has applications that are in considerable demand at present. For example, super-resolution may be desirable for use in converting high-definition (HD) video content, e.g., 1K or 2K resolution video, for viewing on the increasingly popular and commercially available Ultra HD 4K video displays, as well as the next generation of 8K video displays.
Conventional methods for performing super-resolution typically rely on redundancy and explicit motion estimation between video frames to effectively reconstruct a higher resolution signal from many lower resolution measurements. Although such conventional approaches can in principle result in a correct reconstruction of missing detail, their reliance on the quality of estimated motion between frames limits their ability to up-sample unconstrained real-world video with rapid motion, blur, occlusions, drastic appearance changes, and/or presenting other common video processing challenges.